






"""simple docstring"""
import io
import json

import fsspec
import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def 							snake_case (  A__      ,A__		):
			assert isinstance(A__      ,A__		)
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"      ,[False, True]		)
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					int     	=   tmp_path / "cache"
			UpperCAmelCase_	:					Union[str, Any]     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCAmelCase_	:					Tuple     	=   JsonDatasetReader(A__      ,cache_dir=A__      ,keep_in_memory=A__		).read()
			_check_json_dataset(A__      ,A__		)

@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,)
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Union[str, Any]     	=   tmp_path / "cache"
			UpperCAmelCase_	:					List[str]     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_	:					int     	=   features.copy() if features else default_expected_features
			UpperCAmelCase_	:					Dict     	=   (
			    Features({feature: Value(A__		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_	:					List[str]     	=   JsonDatasetReader(A__      ,features=A__      ,cache_dir=A__		).read()
			_check_json_dataset(A__      ,A__		)

@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_3": "float64", "col_1": "string", "col_2": "int64"},
    ]      ,)
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					str     	=   tmp_path / "cache"
			UpperCAmelCase_	:					List[Any]     	=   {"col_3": "float64", "col_1": "string", "col_2": "int64"}
			UpperCAmelCase_	:					List[Any]     	=   features.copy() if features else default_expected_features
			UpperCAmelCase_	:					List[str]     	=   (
			    Features({feature: Value(A__		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_	:					Optional[int]     	=   JsonDatasetReader(A__      ,features=A__      ,cache_dir=A__		).read()
			assert isinstance(A__      ,A__		)
			assert dataset.num_rows == 2
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_3", "col_1", "col_2"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype

def 							snake_case (  A__      ,A__		):
			# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
			UpperCAmelCase_	:					Optional[int]     	=   {"col_2": "int64", "col_3": "float64", "col_1": "string"}
			UpperCAmelCase_	:					Tuple     	=   features.copy()
			UpperCAmelCase_	:					Any     	=   (
			    Features({feature: Value(A__		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_	:					Dict     	=   tmp_path / "cache"
			UpperCAmelCase_	:					Tuple     	=   JsonDatasetReader(A__      ,features=A__      ,cache_dir=A__		).read()
			assert isinstance(A__      ,A__		)
			assert dataset.num_rows == 2
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_2", "col_3", "col_1"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"		), "train", "test"]		)
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[str]     	=   tmp_path / "cache"
			UpperCAmelCase_	:					Union[str, Any]     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_	:					Any     	=   JsonDatasetReader(A__      ,cache_dir=A__      ,split=A__		).read()
			_check_json_dataset(A__      ,A__		)
			assert dataset.split == split if split else "train"

@pytest.mark.parametrize("path_type"      ,[str, list]		)
def 							snake_case (  A__      ,A__      ,A__		):
			if issubclass(A__      ,A__		):
						UpperCAmelCase_	:					Tuple     	=   jsonl_path
			elif issubclass(A__      ,A__		):
						UpperCAmelCase_	:					int     	=   [jsonl_path]
			UpperCAmelCase_	:					Dict     	=   tmp_path / "cache"
			UpperCAmelCase_	:					str     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_	:					List[str]     	=   JsonDatasetReader(A__      ,cache_dir=A__		).read()
			_check_json_dataset(A__      ,A__		)

def 							snake_case (  A__      ,A__      ,A__=("train",)		):
			assert isinstance(A__      ,A__		)
			for split in splits:
						UpperCAmelCase_	:					Union[str, Any]     	=   dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("keep_in_memory"      ,[False, True]		)
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					List[str]     	=   tmp_path / "cache"
			UpperCAmelCase_	:					str     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						UpperCAmelCase_	:					List[Any]     	=   JsonDatasetReader({"train": jsonl_path}      ,cache_dir=A__      ,keep_in_memory=A__		).read()
			_check_json_datasetdict(A__      ,A__		)

@pytest.mark.parametrize(
    "features"      ,[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]      ,)
def 							snake_case (  A__      ,A__      ,A__		):
			UpperCAmelCase_	:					Dict     	=   tmp_path / "cache"
			UpperCAmelCase_	:					Union[str, Any]     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_	:					str     	=   features.copy() if features else default_expected_features
			UpperCAmelCase_	:					Any     	=   (
			    Features({feature: Value(A__		) for feature, dtype in features.items()}		) if features is not None else None
			)
			UpperCAmelCase_	:					Optional[int]     	=   JsonDatasetReader({"train": jsonl_path}      ,features=A__      ,cache_dir=A__		).read()
			_check_json_datasetdict(A__      ,A__		)

@pytest.mark.parametrize("split"      ,[None, NamedSplit("train"		), "train", "test"]		)
def 							snake_case (  A__      ,A__      ,A__		):
			if split:
						UpperCAmelCase_	:					Tuple     	=   {split: jsonl_path}
			else:
						UpperCAmelCase_	:					List[Any]     	=   "train"
						UpperCAmelCase_	:					str     	=   {"train": jsonl_path, "test": jsonl_path}
			UpperCAmelCase_	:					str     	=   tmp_path / "cache"
			UpperCAmelCase_	:					int     	=   {"col_1": "string", "col_2": "int64", "col_3": "float64"}
			UpperCAmelCase_	:					Dict     	=   JsonDatasetReader(A__      ,cache_dir=A__		).read()
			_check_json_datasetdict(A__      ,A__      ,splits=list(path.keys()		)		)
			assert all(dataset[split].split == split for split in path.keys()		)

def 							snake_case (  A__		):
			return json.load(A__		)

def 							snake_case (  A__		):
			return [json.loads(A__		) for line in buffer]







class      UpperCamelCase_			:





	@pytest.mark.parametrize("lines, load_json_function"      ,      [(True, load_json_lines), (False, load_json)]		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Any      ,      lowerCAmelCase_   :					Optional[int]		)		->     str:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lines=lowerCAmelCase_		).write()
							buffer.seek(0		)
							UpperCAmelCase_	:					Union[str, Any]     	=   load_json_function(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				assert isinstance(exported_content[0]      ,      lowerCAmelCase_		)
				assert len(lowerCAmelCase_		) == 10





	@pytest.mark.parametrize(
	    "orient, container, keys, len_at"      ,      [
	        ("records", list, {"tokens", "labels", "answers", "id"}, None),
	        ("split", dict, {"columns", "data"}, "data"),
	        ("index", dict, set("0123456789"		), None),
	        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
	        ("values", list, None, None),
	        ("table", dict, {"schema", "data"}, "data"),
	    ]      ,      )
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					List[Any]      ,      lowerCAmelCase_   :					Tuple      ,      lowerCAmelCase_   :					List[str]		)		->     List[str]:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lines=lowerCAmelCase_      ,      orient=lowerCAmelCase_		).write()
							buffer.seek(0		)
							UpperCAmelCase_	:					Dict     	=   load_json(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				if keys:
							if container is dict:
										assert exported_content.keys() == keys
							else:
										assert exported_content[0].keys() == keys
				else:
							assert not hasattr(lowerCAmelCase_      ,      "keys"		) and not hasattr(exported_content[0]      ,      "keys"		)
				if len_at:
							assert len(exported_content[len_at]		) == 10
				else:
							assert len(lowerCAmelCase_		) == 10





	@pytest.mark.parametrize("lines, load_json_function"      ,      [(True, load_json_lines), (False, load_json)]		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					Tuple		)		->     Union[str, Any]:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lines=lowerCAmelCase_      ,      num_proc=2		).write()
							buffer.seek(0		)
							UpperCAmelCase_	:					str     	=   load_json_function(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				assert isinstance(exported_content[0]      ,      lowerCAmelCase_		)
				assert len(lowerCAmelCase_		) == 10





	@pytest.mark.parametrize(
	    "orient, container, keys, len_at"      ,      [
	        ("records", list, {"tokens", "labels", "answers", "id"}, None),
	        ("split", dict, {"columns", "data"}, "data"),
	        ("index", dict, set("0123456789"		), None),
	        ("columns", dict, {"tokens", "labels", "answers", "id"}, "tokens"),
	        ("values", list, None, None),
	        ("table", dict, {"schema", "data"}, "data"),
	    ]      ,      )
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[str]      ,      lowerCAmelCase_   :					Union[str, Any]      ,      lowerCAmelCase_   :					Dict		)		->     Optional[Any]:
				with io.BytesIO() as buffer:
							JsonDatasetWriter(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lines=lowerCAmelCase_      ,      orient=lowerCAmelCase_      ,      num_proc=2		).write()
							buffer.seek(0		)
							UpperCAmelCase_	:					Union[str, Any]     	=   load_json(lowerCAmelCase_		)
				assert isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		)
				if keys:
							if container is dict:
										assert exported_content.keys() == keys
							else:
										assert exported_content[0].keys() == keys
				else:
							assert not hasattr(lowerCAmelCase_      ,      "keys"		) and not hasattr(exported_content[0]      ,      "keys"		)
				if len_at:
							assert len(exported_content[len_at]		) == 10
				else:
							assert len(lowerCAmelCase_		) == 10





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Optional[Any]		)		->     List[Any]:
				with pytest.raises(lowerCAmelCase_		):
							with io.BytesIO() as buffer:
										JsonDatasetWriter(lowerCAmelCase_      ,      lowerCAmelCase_      ,      num_proc=0		)





	@pytest.mark.parametrize("compression, extension"      ,      [("gzip", "gz"), ("bz2", "bz2"), ("xz", "xz")]		)
	def 	_SCREAMING_SNAKE_CASE ( self   :					Any      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Tuple		)		->     List[str]:
				UpperCAmelCase_	:					Optional[Any]     	=   tmp_path_factory.mktemp("data"		) / f"""test.json.{extension}"""
				UpperCAmelCase_	:					Dict     	=   str(shared_datadir / f"""test_file.json.{extension}"""		)
				JsonDatasetWriter(lowerCAmelCase_      ,      lowerCAmelCase_      ,      compression=lowerCAmelCase_		).write()

				with fsspec.open(lowerCAmelCase_      ,      "rb"      ,      compression="infer"		) as f:
							UpperCAmelCase_	:					Optional[Any]     	=   f.read()
				with fsspec.open(lowerCAmelCase_      ,      "rb"      ,      compression="infer"		) as f:
							UpperCAmelCase_	:					Optional[int]     	=   f.read()
				assert exported_content == original_content




import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	Any:







 '''simple docstring'''
 # Initialise PyTorch model
 UpperCamelCase			  =      MobileBertConfig.from_json_file(UpperCamelCase_       )
 print(f"""Building PyTorch model from configuration: {config}"""       )
 UpperCamelCase			  =      MobileBertForPreTraining(UpperCamelCase_       )
 # Load weights from tf checkpoint
 UpperCamelCase			  =      load_tf_weights_in_mobilebert(UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )
 # Save pytorch-model
 print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
 torch.save(model.state_dict()							,					UpperCamelCase_       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
     )
     parser.add_argument(
         """--mobilebert_config_file""",
         default=None,
         type=str,
         required=True,
         help=(
             """The config json file corresponding to the pre-trained MobileBERT model. \n"""
             """This specifies the model architecture."""
         ),
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     _SCREAMING_SNAKE_CASE 		=      parser.parse_args()
     convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
